1. Field of the Invention
The present invention relates to a machine learning method and a machine learning device for learning fault conditions, and a fault prediction device and a fault prediction system including the machine learning device.
2. Description of the Related Art
In an industrial machine, to improve the yield and prevent accidents, abnormality of any component may be detected in advance. For example, one known method compares the output value of a sensor with a predetermined threshold to detect an abnormality on the basis of the comparison result. An “industrial machine” herein refers to not only industrial robots and machines controlled by CNC (Computer Numerical Control) devices but also machines including service robots and various mechanical devices.
Japanese Laid-open Patent Publication No. S63(1988)-123105 discloses a fault prediction diagnosis method for comparing the operation pattern of a robot in operation with a reference operation pattern for the robot in the normal state to predict a fault of the robot.
Japanese Laid-open Patent Publication No. H10(1998)-039908 discloses a fault prediction method for comparing the difference between the load-side power based on the actual operation state of a drive shaft and the driving-side power based on an operation command issued to the drive shaft with a determination value to evaluate whether a robot mechanism portion has deteriorated and the level of deterioration.
Unfortunately, with greater complexity and sophistication of industrial machines, factors which lead to faults are becoming more complicated. Therefore, conventional fault prediction methods performed in accordance with a predetermined procedures are often inapplicable to actual circumstances or deficient in accuracy. This has triggered a demand for a fault prediction device capable of accurate fault prediction according to the circumstances involved.